TAILIEUCHUNG - Báo cáo khoa học: "Ambiguity Resolution in the DMTRANS PLUS"

We present a cost-based (or energy-based) model of disambiguation. When a sentence is ambiguous, a parse with the least cost is chosen from among multiple hypotheses. Each hypothesis is assigned a cost which is added when: (1) a new instance is created to satisfy reference success, (2) links between instances are created or removed to satisfy constraints on concept sequences, and (3) a concept node with insufficient priming is used for further processing. This method of ambiguity resolution is implemented in DMT~NS PLUS, which is a second generation bi-direetional English/Japanese machine translation system based on a massively parallel spreading. | Ambiguity Resolution in the DmTrans Plus Hiroaki Kitano Hideto Tomabechi and Lori Levin Center for Machine Translation Carnegie Mellon University Pittsburgh PA 15213 . Abstract We present a cost-based or energy-based model of disambiguation. When a sentence is ambiguous a parse with the least cost is chosen from among multiple hypotheses. Each hypothesis is assigned a cost which is added when 1 a new instance is created to satisfy reference success 2 links between instances are created or removed to satisfy consữaints on concept sequences and 3 a concept node with insufficient priming is used for further processing. This method of ambiguity resolution is implemented in DmTrans Plus which is a second generation bi-dfrectional English Japanese machine translation system based on a massively parallel spreading activation paradigm developed at the Center for Machine Translation at Carnegie Mellon University. 1 Introduction One of the central issues in natural language understanding research is ambiguity resolution. Since many sentences are ambiguous out of context techniques for ambiguity resolution have been an important topic in natural language understanding. In this paper we describe a model of ambiguity resolution implemented in DmTrans Plus which is a next generation machine translation system based on a massively parallel comuputational paradigm. In our model ambiguities are resolved by evaluating the cost of each hypothesis the hypothesis with the least cost will be selected. Costs are assigned when 1 a new instance is created to satisfy reference success 2 links between instances are created or removed to satisfy constraints on concept sequences and 3 a concept node with insufficient priming is used for further processing. The underlying philosophy of the model is to view parsing as a dynamic physical process in which one trajectory is taken from among many other possible paths. Thus our notion of the cost of the hypothesis is a representation of the .

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